{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7AEbLs-UEhH",
        "outputId": "72ab760e-5c16-47d7-efe4-f284dabd92a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow-gpu\n",
            "  Using cached tensorflow-gpu-2.12.0.tar.gz (2.6 kB)\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25herror\n",
            "\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n",
            "\n",
            "\u001b[31m×\u001b[0m Encountered error while generating package metadata.\n",
            "\u001b[31m╰─>\u001b[0m See above for output.\n",
            "\n",
            "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
            "\u001b[1;36mhint\u001b[0m: See above for details.\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow-gpu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B9nLBABsU8rX",
        "outputId": "b3a43cb7-dad8-43c7-b860-eb1af21a6ce4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[name: \"/device:CPU:0\"\n",
            "device_type: \"CPU\"\n",
            "memory_limit: 268435456\n",
            "locality {\n",
            "}\n",
            "incarnation: 7849999838779980990\n",
            "xla_global_id: -1\n",
            ", name: \"/device:GPU:0\"\n",
            "device_type: \"GPU\"\n",
            "memory_limit: 14626652160\n",
            "locality {\n",
            "  bus_id: 1\n",
            "  links {\n",
            "  }\n",
            "}\n",
            "incarnation: 14572174331698361200\n",
            "physical_device_desc: \"device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\"\n",
            "xla_global_id: 416903419\n",
            "]\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.python.client import device_lib\n",
        "print(device_lib.list_local_devices())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1UuGJ-47VQYX"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kbIvytG1VMHZ",
        "outputId": "cd653078-3f2b-4b9c-9c13-92c0164d93a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Device mapping:\n",
            "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\n",
            "\n"
          ]
        }
      ],
      "source": [
        "sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FeTEX0p5olIf"
      },
      "source": [
        "#Датасет\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x9ySlmrA8bmp"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w-rX9Mgs8cOZ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NvUFj-anore-"
      },
      "source": [
        "в качестве набора данных был выбран датасет PTB-XL, по тем сооброжениям, что это самый популярный датасет, по нему в целом много информации и есть документация.\n",
        "Также"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQO0_5z3pjg3"
      },
      "source": [
        "Далее код для получения даных\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KlaiYG6JLIQb"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "from google.colab import drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oIc6wGIrLVOo",
        "outputId": "b0b58e16-509b-4627-c97c-36c8d1ca0842"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "drive.mount(\"/content/drive\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RaxkhWfXL1CL"
      },
      "outputs": [],
      "source": [
        "z_file = '/content/drive/MyDrive/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.1.zip'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bGm2L8gKL63N"
      },
      "outputs": [],
      "source": [
        "z = zipfile.ZipFile(z_file, 'r')\n",
        "z.extractall()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6aIoi-0L9wI",
        "outputId": "2120a816-1724-46bc-8f14-b54bb3e8d51f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: wfdb==4.0.0 in /usr/local/lib/python3.10/dist-packages (4.0.0)\n",
            "Requirement already satisfied: SoundFile<0.12.0,>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from wfdb==4.0.0) (0.11.0)\n",
            "Requirement already satisfied: matplotlib<4.0.0,>=3.2.2 in /usr/local/lib/python3.10/dist-packages (from wfdb==4.0.0) (3.7.1)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.10.1 in /usr/local/lib/python3.10/dist-packages (from wfdb==4.0.0) (1.25.2)\n",
            "Requirement already satisfied: pandas<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from wfdb==4.0.0) (1.5.3)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from wfdb==4.0.0) (2.31.0)\n",
            "Requirement already satisfied: scipy<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from wfdb==4.0.0) (1.11.4)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (4.51.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (24.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<2.0.0,>=1.0.0->wfdb==4.0.0) (2023.4)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.8.1->wfdb==4.0.0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.8.1->wfdb==4.0.0) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.8.1->wfdb==4.0.0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.8.1->wfdb==4.0.0) (2024.2.2)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.10/dist-packages (from SoundFile<0.12.0,>=0.10.0->wfdb==4.0.0) (1.16.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->SoundFile<0.12.0,>=0.10.0->wfdb==4.0.0) (2.22)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib<4.0.0,>=3.2.2->wfdb==4.0.0) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install wfdb==4.0.0\n",
        "import wfdb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QefA2YpwMAEi"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import ast\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow.keras as keras\n",
        "\n",
        "sns.set_style('darkgrid')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oSdwb-VTpr4s"
      },
      "source": [
        "Создаем датафрейм с помощью библиотеки pandas\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gFwkLhjhp1BG"
      },
      "outputs": [],
      "source": [
        "PATH_TO_DATA = '/content/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.1'\n",
        "\n",
        "ECG_df = pd.read_csv(os.path.join(PATH_TO_DATA, 'ptbxl_database.csv'), index_col='ecg_id')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xSaOWjtwuUiR"
      },
      "source": [
        "#Предобработка"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IPw_pFNtp9X_"
      },
      "source": [
        "Делаем приведение даных к нужным типам"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZ67VoMWqHws"
      },
      "source": [
        "Создаем еще один датафрейм, в которм хранятся диагнозы SCP_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hfaZecOBqFOG"
      },
      "outputs": [],
      "source": [
        "ECG_df.scp_codes = ECG_df.scp_codes.apply(lambda x: ast.literal_eval(x))\n",
        "ECG_df.patient_id = ECG_df.patient_id.astype(int)\n",
        "ECG_df.nurse = ECG_df.nurse.astype('Int64')\n",
        "ECG_df.site = ECG_df.site.astype('Int64')\n",
        "ECG_df.validated_by = ECG_df.validated_by.astype('Int64')\n",
        "\n",
        "SCP_df = pd.read_csv(os.path.join(PATH_TO_DATA, 'scp_statements.csv'), index_col=0)\n",
        "SCP_df = SCP_df[SCP_df.diagnostic == 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ANNi0-DOMIrM"
      },
      "outputs": [],
      "source": [
        "def diagnostic_class(scp):\n",
        "    res = set()\n",
        "    for k in scp.keys():\n",
        "        if k in SCP_df.index:\n",
        "            res.add(SCP_df.loc[k].diagnostic_class)\n",
        "    return list(res)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_U1EQR-mMJW1"
      },
      "outputs": [],
      "source": [
        "ECG_df['scp_classes'] = ECG_df.scp_codes.apply(diagnostic_class)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgGLVa3JqYB_"
      },
      "source": [
        "Далее код для загрузки непосредственно данных ЭКГ при помощи специальной библиотеки wfdb предназначенной для обработки и построения графиков физиологических сигналов и аннотаций.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "64jw10L2rI8-"
      },
      "outputs": [],
      "source": [
        "def load_raw_data(df, sampling_rate, path):\n",
        "    if sampling_rate == 100:\n",
        "        data = [wfdb.rdsamp(os.path.join(path, f)) for f in df.filename_lr]\n",
        "    else:\n",
        "        data = [wfdb.rdsamp(os.path.join(path, f)) for f in df.filename_hr]\n",
        "    data = np.array([signal for signal, meta in data])\n",
        "    return data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n-dCndjvrL2T"
      },
      "outputs": [],
      "source": [
        "sampling_rate = 100\n",
        "\n",
        "ECG_data = load_raw_data(ECG_df, sampling_rate, PATH_TO_DATA)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "daeY1K6LrNUD",
        "outputId": "6effa124-8f19-4234-8e6c-68d3638f7b14"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sample = ECG_data[0]\n",
        "bar, axes = plt.subplots(sample.shape[1], 1, figsize=(20,10))\n",
        "for i in range(sample.shape[1]):\n",
        "    sns.lineplot(x=np.arange(sample.shape[0]), y=sample[:, i], ax=axes[i])\n",
        "# plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 928
        },
        "id": "fM8SUya5rPV9",
        "outputId": "16caa63f-6b2d-497b-a0cf-60f5de87bdd6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import missingno as msno\n",
        "\n",
        "msno.matrix(ECG_df)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7edtJu1rUNa",
        "outputId": "6ab0314a-f102-401d-a192-3613d1cdf65a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "patient_id                      18885\n",
              "age                                94\n",
              "sex                                 2\n",
              "height                             77\n",
              "weight                            127\n",
              "nurse                              12\n",
              "site                               51\n",
              "device                             11\n",
              "recording_date                  21813\n",
              "report                           9883\n",
              "heart_axis                          8\n",
              "infarction_stadium1                 6\n",
              "infarction_stadium2                 3\n",
              "validated_by                       12\n",
              "second_opinion                      2\n",
              "initial_autogenerated_report        2\n",
              "validated_by_human                  2\n",
              "baseline_drift                    321\n",
              "static_noise                      124\n",
              "burst_noise                       103\n",
              "electrodes_problems                14\n",
              "extra_beats                       128\n",
              "pacemaker                           4\n",
              "strat_fold                         10\n",
              "filename_lr                     21837\n",
              "filename_hr                     21837\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "ECG_df[[col for col in ECG_df.columns if col not in ('scp_codes', 'scp_classes')]].nunique(dropna=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7RsypSyraku"
      },
      "source": [
        "Далее задаем целевые переменные"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjIYKoHJo0IZ"
      },
      "source": [
        "Создаем дополниетльные признаки\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "hr7Jn26SozJC",
        "outputId": "f684a62b-7859-4797-f94b-c65f26068553"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         age  sex  height  weight  infarction_stadium1  infarction_stadium2  \\\n",
              "ecg_id                                                                        \n",
              "1       56.0  1.0     0.0    63.0                  0.0                  0.0   \n",
              "2       19.0  0.0     0.0    70.0                  0.0                  0.0   \n",
              "3       37.0  1.0     0.0    69.0                  0.0                  0.0   \n",
              "4       24.0  0.0     0.0    82.0                  0.0                  0.0   \n",
              "5       19.0  1.0     0.0    70.0                  0.0                  0.0   \n",
              "...      ...  ...     ...     ...                  ...                  ...   \n",
              "21833   67.0  1.0     0.0     0.0                  0.0                  0.0   \n",
              "21834   93.0  0.0     0.0     0.0                  4.0                  0.0   \n",
              "21835   59.0  1.0     0.0     0.0                  0.0                  0.0   \n",
              "21836   64.0  1.0     0.0     0.0                  0.0                  0.0   \n",
              "21837   68.0  0.0     0.0     0.0                  0.0                  0.0   \n",
              "\n",
              "        pacemaker  \n",
              "ecg_id             \n",
              "1             0.0  \n",
              "2             0.0  \n",
              "3             0.0  \n",
              "4             0.0  \n",
              "5             0.0  \n",
              "...           ...  \n",
              "21833         0.0  \n",
              "21834         0.0  \n",
              "21835         0.0  \n",
              "21836         0.0  \n",
              "21837         0.0  \n",
              "\n",
              "[21837 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7e10322b-7207-4217-b53f-24a117baef4c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>infarction_stadium1</th>\n",
              "      <th>infarction_stadium2</th>\n",
              "      <th>pacemaker</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ecg_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>56.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21833</th>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21834</th>\n",
              "      <td>93.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21835</th>\n",
              "      <td>59.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21836</th>\n",
              "      <td>64.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21837</th>\n",
              "      <td>68.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21837 rows × 7 columns</p>\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 21837,\n  \"fields\": [\n    {\n      \"column\": \"ecg_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6303,\n        \"min\": 1,\n        \"max\": 21837,\n        \"num_unique_values\": 21837,\n        \"samples\": [\n          719,\n          20102,\n          9470\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.342738914838762,\n        \"min\": 0.0,\n        \"max\": 95.0,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          76.0,\n          23.0,\n          90.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4995665343853428,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77.99583351804826,\n        \"min\": 0.0,\n        \"max\": 209.0,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          181.0,\n          161.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36.68216488566919,\n        \"min\": 0.0,\n        \"max\": 250.0,\n        \"num_unique_values\": 128,\n        \"samples\": [\n          100.0,\n          77.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"infarction_stadium1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2975516608901823,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"infarction_stadium2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17609104429678668,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pacemaker\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11388894128321064,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "X = pd.DataFrame(index=ECG_df.index)\n",
        "\n",
        "X['age'] = ECG_df.age\n",
        "X.age.fillna(0, inplace=True)\n",
        "\n",
        "X['sex'] = ECG_df.sex.astype(float)\n",
        "X.sex.fillna(0, inplace=True)\n",
        "\n",
        "X['height'] = ECG_df.height\n",
        "X.loc[X.height < 50, 'height'] = np.nan\n",
        "X.height.fillna(0, inplace=True)\n",
        "\n",
        "X['weight'] = ECG_df.weight\n",
        "X.weight.fillna(0, inplace=True)\n",
        "\n",
        "X['infarction_stadium1'] = ECG_df.infarction_stadium1.replace({\n",
        "    'unknown': 0,\n",
        "    'Stadium I': 1,\n",
        "    'Stadium I-II': 2,\n",
        "    'Stadium II': 3,\n",
        "    'Stadium II-III': 4,\n",
        "    'Stadium III': 5\n",
        "}).fillna(0)\n",
        "\n",
        "X['infarction_stadium2'] = ECG_df.infarction_stadium2.replace({\n",
        "    'unknown': 0,\n",
        "    'Stadium I': 1,\n",
        "    'Stadium II': 2,\n",
        "    'Stadium III': 3\n",
        "}).fillna(0)\n",
        "\n",
        "X['pacemaker'] = (ECG_df.pacemaker == 'ja, pacemaker').astype(float)\n",
        "\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "G8-ibMBMsd8P",
        "outputId": "30178775-4af6-48fb-bbe9-c677575bbcec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        NORM  MI  STTC  CD  HYP\n",
              "ecg_id                         \n",
              "1          1   0     0   0    0\n",
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              "3          1   0     0   0    0\n",
              "4          1   0     0   0    0\n",
              "5          1   0     0   0    0\n",
              "...      ...  ..   ...  ..  ...\n",
              "21833      0   0     1   0    0\n",
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              "21835      0   0     1   0    0\n",
              "21836      1   0     0   0    0\n",
              "21837      1   0     0   0    0\n",
              "\n",
              "[21837 rows x 5 columns]"
            ],
            "text/html": [
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              "<p>21837 rows × 5 columns</p>\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-403c100b-298e-4fbd-b27f-c6c55ceec308 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-403c100b-298e-4fbd-b27f-c6c55ceec308');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bd909bf6-2bc1-4759-8dce-8b3f2eba7f1f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bd909bf6-2bc1-4759-8dce-8b3f2eba7f1f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bd909bf6-2bc1-4759-8dce-8b3f2eba7f1f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Z",
              "summary": "{\n  \"name\": \"Z\",\n  \"rows\": 21837,\n  \"fields\": [\n    {\n      \"column\": \"ecg_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6303,\n        \"min\": 1,\n        \"max\": 21837,\n        \"num_unique_values\": 21837,\n        \"samples\": [\n          719,\n          20102,\n          9470\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NORM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"STTC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HYP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "Z = pd.DataFrame(0, index=ECG_df.index, columns=['NORM', 'MI', 'STTC', 'CD', 'HYP'], dtype='int')\n",
        "for i in Z.index:\n",
        "    for k in ECG_df.loc[i].scp_classes:\n",
        "        Z.loc[i, k] = 1\n",
        "\n",
        "Z"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eVihQIymsnZg",
        "outputId": "3dfef5eb-f598-4f7d-dc6b-aead8046b497"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(17441, 7) (17441, 1000, 12) (17441, 5)\n",
            "(2193, 7) (2193, 1000, 12) (2193, 5)\n",
            "(2203, 7) (2203, 1000, 12) (2203, 5)\n"
          ]
        }
      ],
      "source": [
        "X_train, Y_train, Z_train = X[ECG_df.strat_fold <= 8],  ECG_data[X[ECG_df.strat_fold <= 8].index - 1],  Z[ECG_df.strat_fold <= 8]\n",
        "X_valid, Y_valid, Z_valid = X[ECG_df.strat_fold == 9],  ECG_data[X[ECG_df.strat_fold == 9].index - 1],  Z[ECG_df.strat_fold == 9]\n",
        "X_test,  Y_test,  Z_test  = X[ECG_df.strat_fold == 10], ECG_data[X[ECG_df.strat_fold == 10].index - 1], Z[ECG_df.strat_fold == 10]\n",
        "\n",
        "print(X_train.shape, Y_train.shape, Z_train.shape)\n",
        "print(X_valid.shape, Y_valid.shape, Z_valid.shape)\n",
        "print(X_test.shape,  Y_test.shape,  Z_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AjTUoyvLph-x"
      },
      "source": [
        "добавляем стандартизацию\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c_tMs0ebpW3U"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "X_scaler = StandardScaler()\n",
        "X_scaler.fit(X_train)\n",
        "\n",
        "X_train = pd.DataFrame(X_scaler.transform(X_train), columns=X_train.columns)\n",
        "X_valid = pd.DataFrame(X_scaler.transform(X_valid), columns=X_valid.columns)\n",
        "X_test  = pd.DataFrame(X_scaler.transform(X_test),  columns=X_test.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RkOos40IphI8"
      },
      "outputs": [],
      "source": [
        "Y_scaler = StandardScaler()\n",
        "Y_scaler.fit(Y_train.reshape(-1, Y_train.shape[-1]))\n",
        "\n",
        "Y_train = Y_scaler.transform(Y_train.reshape(-1, Y_train.shape[-1])).reshape(Y_train.shape)\n",
        "Y_valid = Y_scaler.transform(Y_valid.reshape(-1, Y_valid.shape[-1])).reshape(Y_valid.shape)\n",
        "Y_test  = Y_scaler.transform(Y_test.reshape(-1, Y_test.shape[-1])).reshape(Y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6WIzihTuZvm"
      },
      "source": [
        "#Архитектура"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H3_lSckaszXx"
      },
      "source": [
        "Далее сама архитектура. Представлена сверточная нейронная сеть состоящая из двух сверточных слоев, с функцией ошибок бинарная кросс энтропия и оптимизатором адам"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GpLCzAUrpIaU"
      },
      "source": [
        "для набора данных с признаками слздается отдельная нейронная сеть"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OU0lqqIbpHEK"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "def create_X_model(X, *, units=32, dropouts=0.3):\n",
        "    X = tf.keras.layers.Dense(units, activation='relu', name='X_dense_1')(X)\n",
        "    X = tf.keras.layers.Dropout(dropouts, name='X_drop_1')(X)\n",
        "    X = tf.keras.layers.Dense(units, activation='relu', name='X_dense_2')(X)\n",
        "    X = tf.keras.layers.Dropout(dropouts, name='X_drop_2')(X)\n",
        "    return X"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAN6nG0Cp-r0"
      },
      "source": [
        "нейронная сеть использующая только данные признаков без ЭКГ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DF_y8VcHpn0L"
      },
      "outputs": [],
      "source": [
        "def create_model01(X_shape, Z_shape):\n",
        "    X_inputs = keras.Input(X_shape[1:], name='X_inputs')\n",
        "\n",
        "    X = create_X_model(X_inputs)\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_1')(X)\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_2')(X)\n",
        "    X = keras.layers.Dropout(0.5, name='Z_drop_1')(X)\n",
        "    outputs = keras.layers.Dense(Z_shape[-1], activation='sigmoid', name='Z_outputs')(X)\n",
        "\n",
        "    model = keras.Model(inputs=X_inputs, outputs=outputs, name='model01')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rOESit5mptG3",
        "outputId": "4a29ae70-dd27-4b96-8344-d54098ec24db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model01\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " X_inputs (InputLayer)       [(None, 7)]               0         \n",
            "                                                                 \n",
            " X_dense_1 (Dense)           (None, 32)                256       \n",
            "                                                                 \n",
            " X_drop_1 (Dropout)          (None, 32)                0         \n",
            "                                                                 \n",
            " X_dense_2 (Dense)           (None, 32)                1056      \n",
            "                                                                 \n",
            " X_drop_2 (Dropout)          (None, 32)                0         \n",
            "                                                                 \n",
            " Z_dense_1 (Dense)           (None, 64)                2112      \n",
            "                                                                 \n",
            " Z_dense_2 (Dense)           (None, 64)                4160      \n",
            "                                                                 \n",
            " Z_drop_1 (Dropout)          (None, 64)                0         \n",
            "                                                                 \n",
            " Z_outputs (Dense)           (None, 5)                 325       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7909 (30.89 KB)\n",
            "Trainable params: 7909 (30.89 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model01 = create_model01(X_train.shape, Z_train.shape)\n",
        "model01.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'Precision', 'Recall'])\n",
        "model01.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dcy5I8d7qKiz"
      },
      "source": [
        "нейронная сеть для данных ЭКГ"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wC8IHehVvvve"
      },
      "source": [
        "# 2 слоя"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-92JS_FTk9P8"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.ndimage import gaussian_filter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZKxUMeUvOHno"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.ndimage import gaussian_filter\n",
        "\n",
        "\n",
        "example_ecg_data = tf.random.normal(shape=(1000,))\n",
        "\n",
        "\n",
        "def create_Y_model(input_shape=(1000, 1), filters=(32, 64)):\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[0], kernel_size=5, activation='relu', input_shape=input_shape))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[1], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "    model.add(tf.keras.layers.Dense(1))\n",
        "    return model\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cLkYXwoPMKBA"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uTDQgIDdqP33"
      },
      "source": [
        "дополнительный слой в которм конкатенируются обе модели"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eFQDD9-OOQLK"
      },
      "outputs": [],
      "source": [
        "def create_model02(X_shape, Y_shape, Z_shape):\n",
        "    X_inputs = keras.Input(shape=X_shape[1:], name='X_inputs')\n",
        "    Y_inputs = keras.Input(shape=Y_shape[1:], name='Y_inputs')\n",
        "\n",
        "    Y_output = create_Y_model(input_shape=Y_shape[1:], filters=(64, 128))(Y_inputs)\n",
        "\n",
        "    X = create_X_model(X_inputs)\n",
        "\n",
        "    X = keras.layers.Concatenate(name='Z_concat')([X, Y_output])\n",
        "\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_1')(X)\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_2')(X)\n",
        "    X = keras.layers.Dropout(0.5, name='Z_drop_1')(X)\n",
        "    outputs = keras.layers.Dense(Z_shape[-1], activation='sigmoid', name='Z_outputs')(X)\n",
        "\n",
        "    model = keras.Model(inputs=[X_inputs, Y_inputs], outputs=outputs, name='model02')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zuUwkc3Wtd17"
      },
      "outputs": [],
      "source": [
        "model02 = create_model02(X_train.shape, Y_train.shape, Z_train.shape)\n",
        "model02.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'Precision', 'Recall'])\n",
        "model02.summary()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BoUb0F_otg1K"
      },
      "outputs": [],
      "source": [
        "\n",
        "callbacks_list = [\n",
        "    keras.callbacks.EarlyStopping(monitor='val_binary_accuracy', patience=20)\n",
        "]\n",
        "history = model02.fit([X_train, Y_train], Z_train, epochs=10, batch_size=32, callbacks=callbacks_list, validation_data=([X_valid, Y_valid], Z_valid))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g15XG24ewb3g"
      },
      "source": [
        "# 2 слоя tanh\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OtEL5umowb3y"
      },
      "outputs": [],
      "source": [
        "from keras import activations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cETHOj12wb3z"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.ndimage import gaussian_filter\n",
        "\n",
        "\n",
        "example_ecg_data = tf.random.normal(shape=(1000,))\n",
        "\n",
        "\n",
        "def create_Y_modeltanh(input_shape=(1000, 1), filters=(32, 64)):\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[0], kernel_size=5, activation=activations.tanh, input_shape=input_shape))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[1], kernel_size=5, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(128, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.Dense(1))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lF0Huzrqwb3z"
      },
      "outputs": [],
      "source": [
        "def create_model02tanh(X_shape, Y_shape, Z_shape):\n",
        "    X_inputs = keras.Input(shape=X_shape[1:], name='X_inputs')\n",
        "    Y_inputs = keras.Input(shape=Y_shape[1:], name='Y_inputs')\n",
        "\n",
        "    Y_output = create_Y_modeltanh(input_shape=Y_shape[1:], filters=(64, 128))(Y_inputs)\n",
        "\n",
        "    X = create_X_model(X_inputs)\n",
        "\n",
        "    X = keras.layers.Concatenate(name='Z_concat')([X, Y_output])\n",
        "\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_1')(X)\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_2')(X)\n",
        "    X = keras.layers.Dropout(0.5, name='Z_drop_1')(X)\n",
        "    outputs = keras.layers.Dense(Z_shape[-1], activation='sigmoid', name='Z_outputs')(X)\n",
        "\n",
        "    model = keras.Model(inputs=[X_inputs, Y_inputs], outputs=outputs, name='model02')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qQBwxoEvwb30"
      },
      "outputs": [],
      "source": [
        "model02tanh = create_model02tanh(X_train.shape, Y_train.shape, Z_train.shape)\n",
        "model02tanh.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'Precision', 'Recall'])\n",
        "model02tanh.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6CzReVIgwb31"
      },
      "outputs": [],
      "source": [
        "\n",
        "callbacks_list = [\n",
        "    keras.callbacks.EarlyStopping(monitor='val_binary_accuracy', patience=20)\n",
        "]\n",
        "history2tanh = model02tanh.fit([X_train, Y_train], Z_train, epochs=10, batch_size=32, callbacks=callbacks_list, validation_data=([X_valid, Y_valid], Z_valid))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x9Vjf0qJw7AW"
      },
      "source": [
        "# 2 слоя maxpooling 10, 5\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tpw_YEbew7Am"
      },
      "outputs": [],
      "source": [
        "def create_Y_model22(input_shape=(1000, 1), filters=(32, 64)):\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[0], kernel_size=5, activation='relu', input_shape=input_shape))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=10))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[1], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=5))\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "    model.add(tf.keras.layers.Dense(1))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LB6RpuFbw7An"
      },
      "outputs": [],
      "source": [
        "def create_model022(X_shape, Y_shape, Z_shape):\n",
        "    X_inputs = keras.Input(shape=X_shape[1:], name='X_inputs')\n",
        "    Y_inputs = keras.Input(shape=Y_shape[1:], name='Y_inputs')\n",
        "\n",
        "    Y_output = create_Y_model22(input_shape=Y_shape[1:], filters=(64, 128))(Y_inputs)\n",
        "\n",
        "    X = create_X_model(X_inputs)\n",
        "\n",
        "    X = keras.layers.Concatenate(name='Z_concat')([X, Y_output])\n",
        "\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_1')(X)\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_2')(X)\n",
        "    X = keras.layers.Dropout(0.5, name='Z_drop_1')(X)\n",
        "    outputs = keras.layers.Dense(Z_shape[-1], activation='sigmoid', name='Z_outputs')(X)\n",
        "\n",
        "    model = keras.Model(inputs=[X_inputs, Y_inputs], outputs=outputs, name='model02')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UrCc77bzw7Ao"
      },
      "outputs": [],
      "source": [
        "model022 = create_model022(X_train.shape, Y_train.shape, Z_train.shape)\n",
        "model022.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'Precision', 'Recall'])\n",
        "model022.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NBd2Se_yw7Ao"
      },
      "outputs": [],
      "source": [
        "\n",
        "callbacks_list = [\n",
        "    keras.callbacks.EarlyStopping(monitor='val_binary_accuracy', patience=20)\n",
        "]\n",
        "history3 = model03.fit([X_train, Y_train], Z_train, epochs=10, batch_size=32, callbacks=callbacks_list, validation_data=([X_valid, Y_valid], Z_valid))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6GIvB7nMbYA"
      },
      "source": [
        "# 3 слоя\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ieENKmFVMaDI"
      },
      "outputs": [],
      "source": [
        "def create_Y_model3(input_shape=(1000, 1), filters=(32, 64, 128)):\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[0], kernel_size=5, activation='relu', input_shape=input_shape))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[1], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[2], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "    model.add(tf.keras.layers.Dense(1))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fs06KEO2MXiH"
      },
      "outputs": [],
      "source": [
        "def create_model03(X_shape, Y_shape, Z_shape):\n",
        "    X_inputs = keras.Input(shape=X_shape[1:], name='X_inputs')\n",
        "    Y_inputs = keras.Input(shape=Y_shape[1:], name='Y_inputs')\n",
        "\n",
        "    Y_output = create_Y_model3(input_shape=Y_shape[1:], filters=(64, 128, 256))(Y_inputs)\n",
        "\n",
        "    X = create_X_model(X_inputs)\n",
        "\n",
        "    X = keras.layers.Concatenate(name='Z_concat')([X, Y_output])\n",
        "\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_1')(X)\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_2')(X)\n",
        "    X = keras.layers.Dropout(0.5, name='Z_drop_1')(X)\n",
        "    outputs = keras.layers.Dense(Z_shape[-1], activation='sigmoid', name='Z_outputs')(X)\n",
        "\n",
        "    model = keras.Model(inputs=[X_inputs, Y_inputs], outputs=outputs, name='model02')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HsZy77-3MpEY"
      },
      "outputs": [],
      "source": [
        "model03 = create_model03(X_train.shape, Y_train.shape, Z_train.shape)\n",
        "model03.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'Precision', 'Recall'])\n",
        "model03.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1GQGSeaGM4Xo"
      },
      "outputs": [],
      "source": [
        "\n",
        "callbacks_list = [\n",
        "    keras.callbacks.EarlyStopping(monitor='val_binary_accuracy', patience=20)\n",
        "]\n",
        "history3 = model03.fit([X_train, Y_train], Z_train, epochs=10, batch_size=32, callbacks=callbacks_list, validation_data=([X_valid, Y_valid], Z_valid))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zlTe1cxYeSTO"
      },
      "source": [
        "# 4 слоя\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qj4iW-8VeSDH"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h1lkbnI2eVHz"
      },
      "outputs": [],
      "source": [
        "def create_Y_model4(input_shape=(1000, 1), filters=(32, 64, 128, 256)):\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[0], kernel_size=5, activation='relu', input_shape=input_shape))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[1], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[2], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[3], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "    model.add(tf.keras.layers.Dense(1))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GjeDNLPgeVH0"
      },
      "outputs": [],
      "source": [
        "def create_model04(X_shape, Y_shape, Z_shape):\n",
        "    X_inputs = keras.Input(shape=X_shape[1:], name='X_inputs')\n",
        "    Y_inputs = keras.Input(shape=Y_shape[1:], name='Y_inputs')\n",
        "\n",
        "    Y_output = create_Y_model4(input_shape=Y_shape[1:], filters=(64, 128, 256, 528))(Y_inputs)\n",
        "\n",
        "    X = create_X_model(X_inputs)\n",
        "\n",
        "    X = keras.layers.Concatenate(name='Z_concat')([X, Y_output])\n",
        "\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_1')(X)\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_2')(X)\n",
        "    X = keras.layers.Dropout(0.5, name='Z_drop_1')(X)\n",
        "    outputs = keras.layers.Dense(Z_shape[-1], activation='sigmoid', name='Z_outputs')(X)\n",
        "\n",
        "    model = keras.Model(inputs=[X_inputs, Y_inputs], outputs=outputs, name='model04')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0iDLqAw_eVH1"
      },
      "outputs": [],
      "source": [
        "model04 = create_model04(X_train.shape, Y_train.shape, Z_train.shape)\n",
        "model04.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'Precision', 'Recall'])\n",
        "model04.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WDF-v0HAeVH2"
      },
      "outputs": [],
      "source": [
        "\n",
        "callbacks_list = [\n",
        "    keras.callbacks.EarlyStopping(monitor='val_binary_accuracy', patience=20)\n",
        "]\n",
        "history10 = model010.fit([X_train, Y_train], Z_train, epochs=10, batch_size=32, callbacks=callbacks_list, validation_data=([X_valid, Y_valid], Z_valid))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dnqbP4kWwsCF"
      },
      "source": [
        "# 5 слоев relu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zsHULdqnwsCS"
      },
      "outputs": [],
      "source": [
        "from keras import activations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J8ADibkgwsCS"
      },
      "outputs": [],
      "source": [
        "def create_Y_model5(input_shape=(1000, 1), filters=(32, 64, 128, 256, 528)):\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[0], kernel_size=5, activation=activations.relu, input_shape=input_shape))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[1], kernel_size=5, activation=activations.relu))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[2], kernel_size=5, activation=activations.relu))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[3], kernel_size=5, activation=activations.relu))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[4], kernel_size=5, activation=activations.relu))\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(128, activation=activations.relu))\n",
        "    model.add(tf.keras.layers.Dense(64))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T_DsogDGiJb8"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import activations, layers, models, Input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GszWb7l1wsCT"
      },
      "outputs": [],
      "source": [
        "def create_model05(Y_shape, Z_shape):\n",
        "    Y_inputs = keras.Input(shape=Y_shape[1:], name='Y_inputs')\n",
        "\n",
        "    Y_output = create_Y_model5(input_shape=Y_shape[1:], filters=(32, 64, 128, 256, 528))(Y_inputs)\n",
        "    Y_output = keras.layers.Dense(64, activation='relu', name='Z_dense_1')(Y_output)\n",
        "    Y_output = keras.layers.Dense(64, activation='relu', name='Z_dense_2')(Y_output)\n",
        "    Y_output = keras.layers.Dropout(0.5, name='Z_drop_1')(Y_output)\n",
        "    outputs = keras.layers.Dense(Z_shape[-1], activation='sigmoid', name='Z_outputs')(Y_output)\n",
        "\n",
        "    model = keras.Model(inputs= Y_inputs, outputs=outputs, name='model05')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7CpOrwNwsCU",
        "outputId": "586f3e8c-8242-4705-82a3-985d79288b9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model05\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " Y_inputs (InputLayer)       [(None, 1000, 12)]        0         \n",
            "                                                                 \n",
            " sequential (Sequential)     (None, 64)                4551728   \n",
            "                                                                 \n",
            " Z_dense_1 (Dense)           (None, 64)                4160      \n",
            "                                                                 \n",
            " Z_dense_2 (Dense)           (None, 64)                4160      \n",
            "                                                                 \n",
            " Z_drop_1 (Dropout)          (None, 64)                0         \n",
            "                                                                 \n",
            " Z_outputs (Dense)           (None, 5)                 325       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4560373 (17.40 MB)\n",
            "Trainable params: 4560373 (17.40 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model05 = create_model05(Y_train.shape, Z_train.shape)\n",
        "model05.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'Precision', 'Recall'])\n",
        "model05.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "se_bW-9Vf4GV"
      },
      "outputs": [],
      "source": [
        "tf.keras.utils.plot_model(model05,show_shapes=True,\n",
        "    show_dtype=True,\n",
        "    show_layer_names=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Rvu1CGhwsCU",
        "outputId": "8c769c07-101f-4f92-9078-a5f5bf2e6b9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "546/546 [==============================] - 23s 23ms/step - loss: 0.4347 - binary_accuracy: 0.8057 - precision: 0.6864 - recall: 0.4375 - val_loss: 0.3873 - val_binary_accuracy: 0.8432 - val_precision: 0.7448 - val_recall: 0.5883\n",
            "Epoch 2/10\n",
            "546/546 [==============================] - 11s 20ms/step - loss: 0.3497 - binary_accuracy: 0.8565 - precision: 0.7638 - recall: 0.6322 - val_loss: 0.3243 - val_binary_accuracy: 0.8610 - val_precision: 0.7672 - val_recall: 0.6550\n",
            "Epoch 3/10\n",
            "546/546 [==============================] - 8s 14ms/step - loss: 0.3179 - binary_accuracy: 0.8709 - precision: 0.7869 - recall: 0.6769 - val_loss: 0.3521 - val_binary_accuracy: 0.8613 - val_precision: 0.7714 - val_recall: 0.6500\n",
            "Epoch 4/10\n",
            "546/546 [==============================] - 7s 12ms/step - loss: 0.3000 - binary_accuracy: 0.8799 - precision: 0.8037 - recall: 0.6995 - val_loss: 0.3214 - val_binary_accuracy: 0.8673 - val_precision: 0.7543 - val_recall: 0.7132\n",
            "Epoch 5/10\n",
            "546/546 [==============================] - 7s 13ms/step - loss: 0.2824 - binary_accuracy: 0.8881 - precision: 0.8182 - recall: 0.7213 - val_loss: 0.3592 - val_binary_accuracy: 0.8471 - val_precision: 0.7144 - val_recall: 0.6693\n",
            "Epoch 6/10\n",
            "546/546 [==============================] - 7s 12ms/step - loss: 0.2636 - binary_accuracy: 0.8946 - precision: 0.8295 - recall: 0.7382 - val_loss: 0.3259 - val_binary_accuracy: 0.8644 - val_precision: 0.7690 - val_recall: 0.6711\n",
            "Epoch 7/10\n",
            "546/546 [==============================] - 7s 13ms/step - loss: 0.2454 - binary_accuracy: 0.9042 - precision: 0.8449 - recall: 0.7641 - val_loss: 0.3248 - val_binary_accuracy: 0.8694 - val_precision: 0.7636 - val_recall: 0.7085\n",
            "Epoch 8/10\n",
            "546/546 [==============================] - 7s 12ms/step - loss: 0.2159 - binary_accuracy: 0.9161 - precision: 0.8622 - recall: 0.7985 - val_loss: 0.3635 - val_binary_accuracy: 0.8632 - val_precision: 0.7582 - val_recall: 0.6825\n",
            "Epoch 9/10\n",
            "546/546 [==============================] - 7s 13ms/step - loss: 0.1902 - binary_accuracy: 0.9271 - precision: 0.8802 - recall: 0.8265 - val_loss: 0.4303 - val_binary_accuracy: 0.8617 - val_precision: 0.7332 - val_recall: 0.7217\n",
            "Epoch 10/10\n",
            "546/546 [==============================] - 7s 12ms/step - loss: 0.1591 - binary_accuracy: 0.9401 - precision: 0.9014 - recall: 0.8591 - val_loss: 0.4732 - val_binary_accuracy: 0.8598 - val_precision: 0.7484 - val_recall: 0.6803\n"
          ]
        }
      ],
      "source": [
        "\n",
        "callbacks_list = [\n",
        "    keras.callbacks.EarlyStopping(monitor='val_binary_accuracy', patience=20)\n",
        "]\n",
        "history5 = model05.fit(Y_train, Z_train, epochs=10, batch_size=32, callbacks=callbacks_list, validation_data=( Y_valid, Z_valid))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ptKt7g6xX5s"
      },
      "source": [
        "# 5 слоев relu avgpooling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "34aAjrY0xX57"
      },
      "outputs": [],
      "source": [
        "from keras import activations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "McMzvRFexX57"
      },
      "outputs": [],
      "source": [
        "def create_Y_model5t(input_shape=(1000, 1), filters=(32, 64, 128, 256, 528)):\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[0], kernel_size=5, activation=activations.tanh, input_shape=input_shape))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[1], kernel_size=5, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[2], kernel_size=5, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[3], kernel_size=5, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[4], kernel_size=5, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(128, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.Dense(64))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RYEQRTGUxX58"
      },
      "outputs": [],
      "source": [
        "def create_model05t(Y_shape, Z_shape):\n",
        "    Y_inputs = keras.Input(shape=Y_shape[1:], name='Y_inputs')\n",
        "\n",
        "    Y_output = create_Y_model5(input_shape=Y_shape[1:], filters=(32, 64, 128, 256, 528))(Y_inputs)\n",
        "    Y_output = keras.layers.Dense(64, activation='relu', name='Z_dense_1')(Y_output)\n",
        "    Y_output = keras.layers.Dense(64, activation='relu', name='Z_dense_2')(Y_output)\n",
        "    Y_output = keras.layers.Dropout(0.5, name='Z_drop_1')(Y_output)\n",
        "    outputs = keras.layers.Dense(Z_shape[-1], activation='sigmoid', name='Z_outputs')(Y_output)\n",
        "\n",
        "    model = keras.Model(inputs= Y_inputs, outputs=outputs, name='model05')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8bAWpBeoxX58"
      },
      "outputs": [],
      "source": [
        "model05t = create_model05t(Y_train.shape, Z_train.shape)\n",
        "model05t.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'Precision', 'Recall'])\n",
        "model05t.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EG6icyjFxX59",
        "outputId": "54992acd-d18f-48a3-f447-75b1859a340f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "546/546 [==============================] - 12s 15ms/step - loss: 0.4255 - binary_accuracy: 0.8133 - precision: 0.6994 - recall: 0.4686 - val_loss: 0.3572 - val_binary_accuracy: 0.8544 - val_precision: 0.7492 - val_recall: 0.6468\n",
            "Epoch 2/10\n",
            "546/546 [==============================] - 7s 13ms/step - loss: 0.3475 - binary_accuracy: 0.8586 - precision: 0.7633 - recall: 0.6453 - val_loss: 0.3332 - val_binary_accuracy: 0.8590 - val_precision: 0.7800 - val_recall: 0.6247\n",
            "Epoch 3/10\n",
            "546/546 [==============================] - 8s 14ms/step - loss: 0.3208 - binary_accuracy: 0.8709 - precision: 0.7869 - recall: 0.6767 - val_loss: 0.3195 - val_binary_accuracy: 0.8669 - val_precision: 0.7831 - val_recall: 0.6632\n",
            "Epoch 4/10\n",
            "546/546 [==============================] - 7s 12ms/step - loss: 0.2987 - binary_accuracy: 0.8823 - precision: 0.8065 - recall: 0.7079 - val_loss: 0.3163 - val_binary_accuracy: 0.8715 - val_precision: 0.7951 - val_recall: 0.6700\n",
            "Epoch 5/10\n",
            "546/546 [==============================] - 7s 13ms/step - loss: 0.2821 - binary_accuracy: 0.8883 - precision: 0.8169 - recall: 0.7238 - val_loss: 0.3197 - val_binary_accuracy: 0.8720 - val_precision: 0.7810 - val_recall: 0.6935\n",
            "Epoch 6/10\n",
            "546/546 [==============================] - 7s 12ms/step - loss: 0.2592 - binary_accuracy: 0.8980 - precision: 0.8356 - recall: 0.7466 - val_loss: 0.3333 - val_binary_accuracy: 0.8729 - val_precision: 0.7749 - val_recall: 0.7085\n",
            "Epoch 7/10\n",
            "546/546 [==============================] - 7s 13ms/step - loss: 0.2364 - binary_accuracy: 0.9084 - precision: 0.8519 - recall: 0.7751 - val_loss: 0.3384 - val_binary_accuracy: 0.8731 - val_precision: 0.7699 - val_recall: 0.7185\n",
            "Epoch 8/10\n",
            "546/546 [==============================] - 7s 12ms/step - loss: 0.2135 - binary_accuracy: 0.9188 - precision: 0.8678 - recall: 0.8035 - val_loss: 0.3570 - val_binary_accuracy: 0.8671 - val_precision: 0.7504 - val_recall: 0.7196\n",
            "Epoch 9/10\n",
            "546/546 [==============================] - 7s 13ms/step - loss: 0.1837 - binary_accuracy: 0.9304 - precision: 0.8875 - recall: 0.8325 - val_loss: 0.4119 - val_binary_accuracy: 0.8662 - val_precision: 0.7607 - val_recall: 0.6953\n",
            "Epoch 10/10\n",
            "546/546 [==============================] - 7s 12ms/step - loss: 0.1555 - binary_accuracy: 0.9421 - precision: 0.9083 - recall: 0.8594 - val_loss: 0.4245 - val_binary_accuracy: 0.8632 - val_precision: 0.7448 - val_recall: 0.7071\n"
          ]
        }
      ],
      "source": [
        "\n",
        "callbacks_list = [\n",
        "    keras.callbacks.EarlyStopping(monitor='val_binary_accuracy', patience=20)\n",
        "]\n",
        "history5t = model05t.fit(Y_train, Z_train, epochs=10, batch_size=32, callbacks=callbacks_list, validation_data=(Y_valid, Z_valid))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLIf_VoDkDAn"
      },
      "source": [
        "# 5 слоев tanh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LuVDiUNciGUE"
      },
      "outputs": [],
      "source": [
        "from keras import activations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qhN6C9sAkHL8"
      },
      "outputs": [],
      "source": [
        "def create_Y_model5(input_shape=(1000, 1), filters=(32, 64, 128, 256, 528)):\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[0], kernel_size=5, activation=activations.tanh, input_shape=input_shape))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[1], kernel_size=5, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[2], kernel_size=5, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[3], kernel_size=5, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[4], kernel_size=5, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(128, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.Dense(1))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f3zazzpxkHL9"
      },
      "outputs": [],
      "source": [
        "def create_model05(X_shape, Y_shape, Z_shape):\n",
        "    X_inputs = keras.Input(shape=X_shape[1:], name='X_inputs')\n",
        "    Y_inputs = keras.Input(shape=Y_shape[1:], name='Y_inputs')\n",
        "\n",
        "    Y_output = create_Y_model5(input_shape=Y_shape[1:], filters=(32, 64, 128, 256, 528))(Y_inputs)\n",
        "\n",
        "    X = create_X_model(X_inputs)\n",
        "\n",
        "    X = keras.layers.Concatenate(name='Z_concat')([X, Y_output])\n",
        "\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_1')(X)\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_2')(X)\n",
        "    X = keras.layers.Dropout(0.5, name='Z_drop_1')(X)\n",
        "    outputs = keras.layers.Dense(Z_shape[-1], activation='sigmoid', name='Z_outputs')(X)\n",
        "\n",
        "    model = keras.Model(inputs=[X_inputs, Y_inputs], outputs=outputs, name='model05')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Np5WiIi0kHL-"
      },
      "outputs": [],
      "source": [
        "model05 = create_model05(X_train.shape, Y_train.shape, Z_train.shape)\n",
        "model05.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'Precision', 'Recall'])\n",
        "model05.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2OKGJwlzkHL-"
      },
      "outputs": [],
      "source": [
        "\n",
        "callbacks_list = [\n",
        "    keras.callbacks.EarlyStopping(monitor='val_binary_accuracy', patience=20)\n",
        "]\n",
        "history5 = model05.fit([X_train, Y_train], Z_train, epochs=10, batch_size=32, callbacks=callbacks_list, validation_data=([X_valid, Y_valid], Z_valid))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kkjxiKXZtnzH"
      },
      "source": [
        "# 6 слоев\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LZcxqV1JtrDT"
      },
      "outputs": [],
      "source": [
        "def create_Y_model6(input_shape=(1000, 1), filters=(32, 64, 128, 128, 256, 528)):\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[0], kernel_size=5, activation='relu', input_shape=input_shape))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[1], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[2], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[3], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[4], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[5], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "    model.add(tf.keras.layers.Dense(1))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FU56TC4etrDl"
      },
      "outputs": [],
      "source": [
        "def create_model06(X_shape, Y_shape, Z_shape):\n",
        "    X_inputs = keras.Input(shape=X_shape[1:], name='X_inputs')\n",
        "    Y_inputs = keras.Input(shape=Y_shape[1:], name='Y_inputs')\n",
        "\n",
        "    Y_output = create_Y_model6(input_shape=Y_shape[1:], filters=(32, 64, 128, 128, 256, 528))(Y_inputs)\n",
        "\n",
        "    X = create_X_model(X_inputs)\n",
        "\n",
        "    X = keras.layers.Concatenate(name='Z_concat')([X, Y_output])\n",
        "\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_1')(X)\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_2')(X)\n",
        "    X = keras.layers.Dropout(0.5, name='Z_drop_1')(X)\n",
        "    outputs = keras.layers.Dense(Z_shape[-1], activation='sigmoid', name='Z_outputs')(X)\n",
        "\n",
        "    model = keras.Model(inputs=[X_inputs, Y_inputs], outputs=outputs, name='model06')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8OaORxw8trDl"
      },
      "outputs": [],
      "source": [
        "model06 = create_model06(X_train.shape, Y_train.shape, Z_train.shape)\n",
        "model06.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'Precision', 'Recall'])\n",
        "model06.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xbRiGPGjtrDm"
      },
      "outputs": [],
      "source": [
        "\n",
        "callbacks_list = [\n",
        "    keras.callbacks.EarlyStopping(monitor='val_binary_accuracy', patience=20)\n",
        "]\n",
        "history6 = model06.fit([X_train, Y_train], Z_train, epochs=10, batch_size=32, callbacks=callbacks_list, validation_data=([X_valid, Y_valid], Z_valid))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KRhmUCMQ4JFY"
      },
      "source": [
        "#7 слоев\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LHykoqmY4MI2"
      },
      "outputs": [],
      "source": [
        "def create_Y_model7(input_shape=(1000, 1), filters=(32, 64, 128, 128, 256, 528)):\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[0], kernel_size=5, activation='relu', input_shape=input_shape))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[1], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[2], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[3], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[4], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[5], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[6], kernel_size=5, activation='relu'))\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "    model.add(tf.keras.layers.Dense(1))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vmicbVO04MI3"
      },
      "outputs": [],
      "source": [
        "def create_model07(X_shape, Y_shape, Z_shape):\n",
        "    X_inputs = keras.Input(shape=X_shape[1:], name='X_inputs')\n",
        "    Y_inputs = keras.Input(shape=Y_shape[1:], name='Y_inputs')\n",
        "\n",
        "    Y_output = create_Y_model7(input_shape=Y_shape[1:], filters=(32, 64, 128, 128,128, 128, 128,128, 256, 528))(Y_inputs)\n",
        "\n",
        "    X = create_X_model(X_inputs)\n",
        "\n",
        "    X = keras.layers.Concatenate(name='Z_concat')([X, Y_output])\n",
        "\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_1')(X)\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_2')(X)\n",
        "    X = keras.layers.Dropout(0.5, name='Z_drop_1')(X)\n",
        "    outputs = keras.layers.Dense(Z_shape[-1], activation='sigmoid', name='Z_outputs')(X)\n",
        "\n",
        "    model = keras.Model(inputs=[X_inputs, Y_inputs], outputs=outputs, name='model06')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9KI6yarq4MI4"
      },
      "outputs": [],
      "source": [
        "model07 = create_model07(X_train.shape, Y_train.shape, Z_train.shape)\n",
        "model07.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'Precision', 'Recall'])\n",
        "model07.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s_dKnElF4MI5"
      },
      "outputs": [],
      "source": [
        "\n",
        "callbacks_list = [\n",
        "    keras.callbacks.EarlyStopping(monitor='val_binary_accuracy', patience=20)\n",
        "]\n",
        "history10 = model010.fit([X_train, Y_train], Z_train, epochs=10, batch_size=32, callbacks=callbacks_list, validation_data=([X_valid, Y_valid], Z_valid))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SXCEw0aGXbcl"
      },
      "outputs": [],
      "source": [
        "from keras import activations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "55yK8g1XVz4y"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.ndimage import gaussian_filter\n",
        "\n",
        "\n",
        "example_ecg_data = tf.random.normal(shape=(1000,))\n",
        "\n",
        "\n",
        "def create_Y_modeltanh(input_shape=(1000, 1), filters=(32, 64)):\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[0], kernel_size=5, activation=activations.tanh, input_shape=input_shape))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Conv1D(filters=filters[1], kernel_size=5, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "    model.add(tf.keras.layers.Flatten())\n",
        "    model.add(tf.keras.layers.Dense(128, activation=activations.tanh))\n",
        "    model.add(tf.keras.layers.Dense(1))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Unc25SSrV2qU"
      },
      "outputs": [],
      "source": [
        "def create_model02tanh(X_shape, Y_shape, Z_shape):\n",
        "    X_inputs = keras.Input(shape=X_shape[1:], name='X_inputs')\n",
        "    Y_inputs = keras.Input(shape=Y_shape[1:], name='Y_inputs')\n",
        "\n",
        "    Y_output = create_Y_modeltanh(input_shape=Y_shape[1:], filters=(64, 128))(Y_inputs)\n",
        "\n",
        "    X = create_X_model(X_inputs)\n",
        "\n",
        "    X = keras.layers.Concatenate(name='Z_concat')([X, Y_output])\n",
        "\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_1')(X)\n",
        "    X = keras.layers.Dense(64, activation='relu', name='Z_dense_2')(X)\n",
        "    X = keras.layers.Dropout(0.5, name='Z_drop_1')(X)\n",
        "    outputs = keras.layers.Dense(Z_shape[-1], activation='sigmoid', name='Z_outputs')(X)\n",
        "\n",
        "    model = keras.Model(inputs=[X_inputs, Y_inputs], outputs=outputs, name='model02')\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "topkitpNV2qU"
      },
      "outputs": [],
      "source": [
        "model02tanh = create_model02tanh(X_train.shape, Y_train.shape, Z_train.shape)\n",
        "model02tanh.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy', 'Precision', 'Recall'])\n",
        "model02tanh.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZLbIKoJtV2qV"
      },
      "outputs": [],
      "source": [
        "\n",
        "callbacks_list = [\n",
        "    keras.callbacks.EarlyStopping(monitor='val_binary_accuracy', patience=20)\n",
        "]\n",
        "history2tanh = model02tanh.fit([X_train, Y_train], Z_train, epochs=10, batch_size=32, callbacks=callbacks_list, validation_data=([X_valid, Y_valid], Z_valid))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eK8A5zU0ueY9"
      },
      "source": [
        "#Результаты"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4f58MMtSuJQP"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "plt.plot(history.history['binary_accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['loss'], label='Loss')\n",
        "plt.plot(history.history['precision'], label='Precision')\n",
        "plt.plot(history.history['recall'], label='Recall')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1sZehTrx1nq"
      },
      "source": [
        "# 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uweJ-gg5uLgF"
      },
      "outputs": [],
      "source": [
        "labels=['NORM', 'MI', 'STTC', 'CD', 'HYP']\n",
        "Z_pred_03 = model03.predict([X_test, Y_test]).round().astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eXrU-VIDuL_n"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "\n",
        "def print_confusion_matrix(confusion_matrix, axes, class_label, class_names, fontsize=14):\n",
        "\n",
        "    df_cm = pd.DataFrame(confusion_matrix, index=class_names, columns=class_names)\n",
        "\n",
        "    try:\n",
        "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\", cbar=False, ax=axes)\n",
        "    except ValueError:\n",
        "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
        "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
        "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
        "    axes.set_ylabel('True label')\n",
        "    axes.set_xlabel('Predicted label')\n",
        "    axes.set_title(\"Class - \" + class_label)\n",
        "\n",
        "fig, ax = plt.subplots(1, 5, figsize=(16, 3))\n",
        "\n",
        "for axes, cfs_matrix, label in zip(ax.flatten(), metrics.multilabel_confusion_matrix(Z_test, Z_pred_03), labels):\n",
        "    print_confusion_matrix(cfs_matrix, axes, label, [\"N\", \"Y\"])\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(metrics.classification_report(Z_test, Z_pred_03, target_names=labels, zero_division=0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yr-K1Mq9mMbr"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yp82IkSlRr4A"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "plt.plot(history.history['binary_accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['loss'], label='Loss')\n",
        "plt.plot(history.history['precision'], label='Precision')\n",
        "plt.plot(history.history['recall'], label='Recall')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loLNcTwhx6PN"
      },
      "source": [
        "# 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V06RQO4MRr4C"
      },
      "outputs": [],
      "source": [
        "labels=['NORM', 'MI', 'STTC', 'CD', 'HYP']\n",
        "Z_pred_02 = model02.predict([X_test, Y_test]).round().astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tPeznTNIRr4D"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "\n",
        "def print_confusion_matrix(confusion_matrix, axes, class_label, class_names, fontsize=14):\n",
        "\n",
        "    df_cm = pd.DataFrame(confusion_matrix, index=class_names, columns=class_names)\n",
        "\n",
        "    try:\n",
        "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\", cbar=False, ax=axes)\n",
        "    except ValueError:\n",
        "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
        "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
        "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
        "    axes.set_ylabel('True label')\n",
        "    axes.set_xlabel('Predicted label')\n",
        "    axes.set_title(\"Class - \" + class_label)\n",
        "\n",
        "fig, ax = plt.subplots(1, 5, figsize=(16, 3))\n",
        "\n",
        "for axes, cfs_matrix, label in zip(ax.flatten(), metrics.multilabel_confusion_matrix(Z_test, Z_pred_02), labels):\n",
        "    print_confusion_matrix(cfs_matrix, axes, label, [\"N\", \"Y\"])\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(metrics.classification_report(Z_test, Z_pred_02, target_names=labels, zero_division=0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c0iLJh3_ZqYu"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pe_PB61rx8ju"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EcNS5hPVZqnm"
      },
      "outputs": [],
      "source": [
        "labels=['NORM', 'MI', 'STTC', 'CD', 'HYP']\n",
        "Z_pred_02tanh = model02tanh.predict([X_test, Y_test]).round().astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IsxcE97bZqno"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "\n",
        "def print_confusion_matrix(confusion_matrix, axes, class_label, class_names, fontsize=14):\n",
        "\n",
        "    df_cm = pd.DataFrame(confusion_matrix, index=class_names, columns=class_names)\n",
        "\n",
        "    try:\n",
        "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\", cbar=False, ax=axes)\n",
        "    except ValueError:\n",
        "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
        "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
        "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
        "    axes.set_ylabel('True label')\n",
        "    axes.set_xlabel('Predicted label')\n",
        "    axes.set_title(\"Class - \" + class_label)\n",
        "\n",
        "fig, ax = plt.subplots(1, 5, figsize=(16, 3))\n",
        "\n",
        "for axes, cfs_matrix, label in zip(ax.flatten(), metrics.multilabel_confusion_matrix(Z_test, Z_pred_02tanh), labels):\n",
        "    print_confusion_matrix(cfs_matrix, axes, label, [\"N\", \"Y\"])\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(metrics.classification_report(Z_test, Z_pred_02tanh, target_names=labels, zero_division=0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7avrFrrWfzS4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjUlycmRx-3Y"
      },
      "source": [
        "# 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CjdBu9ZcfzlW"
      },
      "outputs": [],
      "source": [
        "labels=['NORM', 'MI', 'STTC', 'CD', 'HYP']\n",
        "Z_pred_04 = model04.predict([X_test, Y_test]).round().astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8GnamjdRfzlX"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "\n",
        "def print_confusion_matrix(confusion_matrix, axes, class_label, class_names, fontsize=14):\n",
        "\n",
        "    df_cm = pd.DataFrame(confusion_matrix, index=class_names, columns=class_names)\n",
        "\n",
        "    try:\n",
        "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\", cbar=False, ax=axes)\n",
        "    except ValueError:\n",
        "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
        "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
        "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
        "    axes.set_ylabel('True label')\n",
        "    axes.set_xlabel('Predicted label')\n",
        "    axes.set_title(\"Class - \" + class_label)\n",
        "\n",
        "fig, ax = plt.subplots(1, 5, figsize=(16, 3))\n",
        "\n",
        "for axes, cfs_matrix, label in zip(ax.flatten(), metrics.multilabel_confusion_matrix(Z_test, Z_pred_04), labels):\n",
        "    print_confusion_matrix(cfs_matrix, axes, label, [\"N\", \"Y\"])\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(metrics.classification_report(Z_test, Z_pred_04, target_names=labels, zero_division=0))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJLmTezVloWV"
      },
      "source": [
        "#5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uSvoq7_lsh9",
        "outputId": "da0c01d2-ebed-42f6-e3f4-40feb6b799ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "69/69 [==============================] - 3s 33ms/step\n"
          ]
        }
      ],
      "source": [
        "labels=['NORM', 'MI', 'STTC', 'CD', 'HYP']\n",
        "Z_pred_05 = model05.predict(Y_test).round().astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "7yY0ldpplsh-",
        "outputId": "91d37db5-9195-4e96-8c90-620718029d13"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x300 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        NORM       0.77      0.89      0.83       964\n",
            "          MI       0.72      0.66      0.68       553\n",
            "        STTC       0.76      0.60      0.67       523\n",
            "          CD       0.73      0.66      0.70       498\n",
            "         HYP       0.68      0.25      0.37       263\n",
            "\n",
            "   micro avg       0.75      0.69      0.72      2801\n",
            "   macro avg       0.73      0.61      0.65      2801\n",
            "weighted avg       0.74      0.69      0.70      2801\n",
            " samples avg       0.74      0.71      0.71      2801\n",
            "\n",
            "0.5801180208806174\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "\n",
        "def print_confusion_matrix(confusion_matrix, axes, class_label, class_names, fontsize=14):\n",
        "\n",
        "    df_cm = pd.DataFrame(confusion_matrix, index=class_names, columns=class_names)\n",
        "\n",
        "    try:\n",
        "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\", cbar=False, ax=axes)\n",
        "    except ValueError:\n",
        "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
        "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
        "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
        "    axes.set_ylabel('True label')\n",
        "    axes.set_xlabel('Predicted label')\n",
        "    axes.set_title(\"Class - \" + class_label)\n",
        "\n",
        "fig, ax = plt.subplots(1, 5, figsize=(16, 3))\n",
        "\n",
        "for axes, cfs_matrix, label in zip(ax.flatten(), metrics.multilabel_confusion_matrix(Z_test, Z_pred_05), labels):\n",
        "    print_confusion_matrix(cfs_matrix, axes, label, [\"N\", \"Y\"])\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(metrics.classification_report(Z_test, Z_pred_05, target_names=labels, zero_division=0))\n",
        "print(metrics.accuracy_score(Z_pred_05, Z_test))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "qBVa2FTJJeRJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix = metrics.multilabel_confusion_matrix(Z_test, Z_pred_05)\n",
        "\n",
        "# Нормализуем матрицу по строкам (истинные метки)\n",
        "conf_matrix_normalized = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "# Умножаем на 100 для получения процентов\n",
        "conf_matrix_percentage = conf_matrix_normalized * 100\n",
        "\n",
        "print(\"Confusion Matrix (in percentages):\")\n",
        "print(conf_matrix_percentage)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6o3F4ifJfaW",
        "outputId": "381eae13-d9e6-4a45-ad16-410c2c9a0edf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix (in percentages):\n",
            "[[[90.55912007 22.57194245]\n",
            "  [ 9.44087993 77.42805755]]\n",
            "\n",
            " [[88.79716981 28.40236686]\n",
            "  [11.20283019 71.59763314]]\n",
            "\n",
            " [[88.37339296 23.91304348]\n",
            "  [11.62660704 76.08695652]]\n",
            "\n",
            " [[90.4625928  26.7699115 ]\n",
            "  [ 9.5374072  73.2300885 ]]\n",
            "\n",
            " [[90.6888361  31.63265306]\n",
            "  [ 9.3111639  68.36734694]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix = metrics.multilabel_confusion_matrix(Z_test, Z_pred_06)\n",
        "\n",
        "# Нормализуем матрицу по строкам (истинные метки)\n",
        "conf_matrix_normalized = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "# Умножаем на 100 для получения процентов\n",
        "conf_matrix_percentage = conf_matrix_normalized * 100\n",
        "\n",
        "print(\"Confusion Matrix (in percentages):\")\n",
        "print(conf_matrix_percentage)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mk_wk7B2KO1l",
        "outputId": "4c3c8222-0f62-45eb-ea19-26f8f14dff5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix (in percentages):\n",
            "[[[89.13987837 20.24714829]\n",
            "  [10.86012163 79.75285171]]\n",
            "\n",
            " [[87.42791234 28.57142857]\n",
            "  [12.57208766 71.42857143]]\n",
            "\n",
            " [[89.55916473 28.39248434]\n",
            "  [10.44083527 71.60751566]]\n",
            "\n",
            " [[90.58891455 28.87473461]\n",
            "  [ 9.41108545 71.12526539]]\n",
            "\n",
            " [[91.87192118 43.35260116]\n",
            "  [ 8.12807882 56.64739884]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRhQdjqP2rxV"
      },
      "source": [
        "#6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fjxMBOJ2rxk",
        "outputId": "50b0f53d-0712-4983-9e2d-e1e4c46ee21f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "69/69 [==============================] - 0s 4ms/step\n"
          ]
        }
      ],
      "source": [
        "labels=['NORM', 'MI', 'STTC', 'CD', 'HYP']\n",
        "Z_pred_06 = model05t.predict(Y_test).round().astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "qVnRjGOc2rxk",
        "outputId": "56892d60-65c6-455c-ed71-a279c92fe646"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x300 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        NORM       0.80      0.87      0.83       964\n",
            "          MI       0.71      0.61      0.66       553\n",
            "        STTC       0.72      0.66      0.68       523\n",
            "          CD       0.71      0.67      0.69       498\n",
            "         HYP       0.57      0.37      0.45       263\n",
            "\n",
            "   micro avg       0.74      0.70      0.72      2801\n",
            "   macro avg       0.70      0.64      0.66      2801\n",
            "weighted avg       0.73      0.70      0.71      2801\n",
            " samples avg       0.74      0.71      0.71      2801\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "\n",
        "def print_confusion_matrix(confusion_matrix, axes, class_label, class_names, fontsize=14):\n",
        "\n",
        "    df_cm = pd.DataFrame(confusion_matrix, index=class_names, columns=class_names)\n",
        "\n",
        "    try:\n",
        "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\", cbar=False, ax=axes)\n",
        "    except ValueError:\n",
        "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
        "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
        "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
        "    axes.set_ylabel('True label')\n",
        "    axes.set_xlabel('Predicted label')\n",
        "    axes.set_title(\"Class - \" + class_label)\n",
        "\n",
        "fig, ax = plt.subplots(1, 5, figsize=(16, 3))\n",
        "\n",
        "for axes, cfs_matrix, label in zip(ax.flatten(), metrics.multilabel_confusion_matrix(Z_test, Z_pred_06), labels):\n",
        "    print_confusion_matrix(cfs_matrix, axes, label, [\"N\", \"Y\"])\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(metrics.classification_report(Z_test, Z_pred_06, target_names=labels, zero_division=0))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}